research.nd.edu


The University of Notre Dame hosts a flagship CECAM workshop where leading AI researchers apply machine learning algorithms to predict molecular and material properties. Participants utilize advanced interatomic potentials and foundation models, leveraging extensive property datasets and computational simulations to streamline materials characterization. This collaborative forum fosters knowledge exchange on data-driven predictive frameworks, aiming to accelerate discovery of novel materials for energy, water security, and healthcare applications.

Key points

  • Development of machine learning interatomic potentials for accurate atomic interaction predictions in materials simulations.
  • Use of foundation models trained on large chemical property datasets for transferable molecular property predictions.
  • Integration of ML techniques with IR, UV/Vis, and NMR spectroscopy automates materials characterization workflows.

Why it matters: This workshop accelerates materials discovery by integrating advanced machine learning methods, promising transformative applications in energy, environmental sustainability, and healthcare.

Q&A

  • What is CECAM?
  • What are machine learning interatomic potentials?
  • How do foundation models apply in materials discovery?
  • How is spectroscopy integrated with machine learning?
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Notre Dame hosts major international artificial intelligence and machine learning conference | News | News & Events | Notre Dame Research | University of Notre Dame